On the Importance of Learning Aggregate Posteriors in Multimodal Variational Autoencoders
We study latent variable models of two modalities: images and text. A common task for these multimodal models is to perform conditional generation; for instance, generating an image conditioned on text. This can be achieved by sampling the posterior of the text then generating the image given the latent variable. However, we find that a problem with this approach is that the posterior of the text does not match the posteriors of the images corresponding to that text. The result is that the generated images are either of poor quality or don't match the text. A similar problem is also encountered in the mismatch between the prior and the marginal aggregate posterior. In this paper, we highlight the importance of learning aggregate posteriors when faced with these types of distribution mismatches. We demonstrate this on modified versions of the CLEVR and CelebA datasets. 

Inference Suboptimality in Variational Autoencoders
We analyze approximate inference in variational autoencoders in terms of the approximation and amortization gaps. We find that suboptimal inference is often due to amortizing inference rather than the limited complexity of the approximating distribution. We show that this is due partly to the generator learning to accommodate the choice of approximation. Furthermore, we show that the parameters used to increase the expressiveness of the approximation play a role in generalizing inference rather than simply improving the complexity of the approximation. 

Reinterpreting ImportanceWeighted Autoencoders
The standard interpretation of importanceweighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. We give an alternate interpretation of this procedure: that it optimizes the standard variational lower bound, but using a more complex distribution. We formally derive this result, present a tighter lower bound, and visualize the implicit importanceweighted distribution. 
INNF+ Workshop
I coorganized a workshop at ICML 2020 on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models.


Invertible Neural Networks and Normalizing Flows
I coorganized a workshop at ICML 2019 on Invertible Neural Networks and Normalizing Flows.


Learning to Ignore
An exploration of how to model information that is relevant to a trained network 

Uncertainty in Bayesian Neural Networks
Visualizations of decision boundaries in BNNs 

Approximate Posterior Building Blocks
This is a short review of orthogonal methods for improving inference in latent variable models. I examine the lower bounds and complexities of these models as well as their combinations. 

Gradients of Deep Networks
A small look at skipconnection models 

Intro to Probability for ML
Review of probability theory 
PhD Dissertation: Approximate Inference in Variational Autoencoders
A deep latent variable model is a powerful tool for modelling complex distributions. However, in order to train this model, we often need to perform approximate inference of the latent variable. A variational autoencoder (VAE) is a framework for learning both the generative and inference models for a latent variable model. This thesis provides novel analyses, applications, and interpretations of approximate inference in VAEs. 

MSc Dissertation: Gene Expression Deconvolution with Subpopulation Proportions
Personalized cancer strategies are currently being hindered by intratumor heterogeneity.
One source of heterogeneity, clonal evolution, can lead to genetically distinct subpopulations
within a sample. Through the use of subclonal reconstruction methods, we can obtain estimates
of the subpopulation proportions within a single sample. Here, I leverage these proportion
estimates by incorporating it into the deconvolution of tumour gene expression data in order to
estimate the subclone specific gene expression profiles. 